// Copyright (c) 2018 PaddlePaddle Authors. All Rights Reserved.
//
// Licensed under the Apache License, Version 2.0 (the "License");
// you may not use this file except in compliance with the License.
// You may obtain a copy of the License at
//
//     http://www.apache.org/licenses/LICENSE-2.0
//
// Unless required by applicable law or agreed to in writing, software
// distributed under the License is distributed on an "AS IS" BASIS,
// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
// See the License for the specific language governing permissions and
// limitations under the License.

#include "paddle/fluid/framework/ir/conv_affine_channel_fuse_pass.h"
#include <functional>
#include <string>
#include <vector>
#include "paddle/fluid/framework/lod_tensor.h"
#include "paddle/fluid/operators/math/cpu_vec.h"
#include "paddle/fluid/platform/enforce.h"

namespace paddle {
namespace framework {
namespace ir {

#define GET_CONV_BN_NODES(pattern_name)                                    \
  /* OPERATORS */                                                          \
  GET_IR_NODE_FROM_SUBGRAPH(conv, conv, pattern_name);                     \
  GET_IR_NODE_FROM_SUBGRAPH(affine_channel, affine_channel, pattern_name); \
  /* CONV inputs */                                                        \
  GET_IR_NODE_FROM_SUBGRAPH(conv_weight, conv_weight, pattern_name);       \
  /* CONV outputs */                                                       \
  GET_IR_NODE_FROM_SUBGRAPH(conv_out, conv_out, pattern_name);             \
  /* Affine Channel inputs */                                              \
  GET_IR_NODE_FROM_SUBGRAPH(ac_scale, ac_scale, pattern_name);             \
  GET_IR_NODE_FROM_SUBGRAPH(ac_bias, ac_bias, pattern_name);               \
  /* Affine channel outputs */                                             \
  GET_IR_NODE_FROM_SUBGRAPH(ac_out, ac_out, pattern_name); /* Out */

void recompute_bias_and_weights(const Scope* scope, ir::Node* conv_weight,
                                const ir::Node& ac_scale,
                                const LoDTensor& ac_bias_tensor,
                                LoDTensor* eltwise_y_in_tensor) {
  using EigenVectorArrayMap =
      Eigen::Map<Eigen::Array<float, Eigen::Dynamic, 1>>;
  using ConstEigenVectorArrayMap =
      Eigen::Map<const Eigen::Array<float, Eigen::Dynamic, 1>>;
  using EigenMatrixArrayMap = Eigen::Map<
      Eigen::Array<float, Eigen::Dynamic, Eigen::Dynamic, Eigen::RowMajor>>;

  // Re-compute bias of conv2d from AffineChannel
  PADDLE_ENFORCE_EQ(eltwise_y_in_tensor->dims(), ac_bias_tensor.dims());

  auto* scale_tensor = scope->FindVar(ac_scale.Name())->GetMutable<LoDTensor>();

  ConstEigenVectorArrayMap scale_array(scale_tensor->data<float>(),
                                       scale_tensor->numel(), 1);
  ConstEigenVectorArrayMap ac_bias_array(ac_bias_tensor.data<float>(),
                                         ac_bias_tensor.numel(), 1);

  EigenVectorArrayMap eltwise_y_in_array(
      eltwise_y_in_tensor->mutable_data<float>(platform::CPUPlace()),
      eltwise_y_in_tensor->numel(), 1);

  eltwise_y_in_array = (eltwise_y_in_array * scale_array) + ac_bias_array;

  // Re-compute weight of conv2d from AffineChannel
  auto* weights = scope->FindVar(conv_weight->Name())->GetMutable<LoDTensor>();
  auto weights_shape = weights->dims();
  auto weights_shape_2d = flatten_to_2d(weights_shape, 1);

  EigenMatrixArrayMap weights_array_2d(
      weights->mutable_data<float>(platform::CPUPlace()), weights_shape_2d[0],
      weights_shape_2d[1]);

  weights_array_2d.colwise() *= scale_array;
}

std::unique_ptr<ir::Graph> ConvAffineChannelFusePass::ApplyImpl(
    std::unique_ptr<ir::Graph> graph) const {
  PADDLE_ENFORCE(graph.get());
  FusePassBase::Init(name_scope_, graph.get());

  auto* scope = param_scope();
  PADDLE_ENFORCE(scope);

  GraphPatternDetector gpd;
  auto* conv_input =
      gpd.mutable_pattern()
          ->NewNode(patterns::PDNodeName(name_scope_, "conv_input"))
          ->AsInput()
          ->assert_is_op_input("conv2d", "Input");
  patterns::ConvAffineChannel conv_ac_pattern(gpd.mutable_pattern(),
                                              name_scope_);
  conv_ac_pattern(conv_input, false /*with_eltwise_add*/);

  int found_conv_ac_count = 0;
  auto handler = [&](const GraphPatternDetector::subgraph_t& subgraph,
                     Graph* g) {
    VLOG(4) << "handle ConvAffineChannel fuse";

    GET_CONV_BN_NODES(conv_ac_pattern);

    // check if fuse can be done and if MKL-DNN should be used
    FuseOptions fuse_option = FindFuseOption(*conv, *affine_channel);
    if (fuse_option == DO_NOT_FUSE) {
      VLOG(3) << "do not perform conv+affinechannel fuse";
      return;
    }

    // Create eltwise_y (conv bias) variable
    VarDesc eltwise_y_in_desc(
        patterns::PDNodeName(name_scope_, "eltwise_y_in"));
    eltwise_y_in_desc.SetPersistable(true);
    auto* eltwise_y_in_node = g->CreateVarNode(&eltwise_y_in_desc);
    auto* eltwise_y_in_tensor =
        scope->Var(eltwise_y_in_node->Name())->GetMutable<LoDTensor>();

    // Get affine_channel bias
    auto* ac_bias_tensor =
        scope->FindVar(ac_bias->Name())->GetMutable<LoDTensor>();

    // Initialize eltwise_y
    eltwise_y_in_tensor->Resize(ac_bias_tensor->dims());
    std::fill_n(eltwise_y_in_tensor->mutable_data<float>(platform::CPUPlace()),
                eltwise_y_in_tensor->numel(), 0.0f);

    // update weights and biases
    recompute_bias_and_weights(scope, conv_weight, *ac_scale, *ac_bias_tensor,
                               eltwise_y_in_tensor);

    // create an elementwise add node.
    OpDesc desc;
    desc.SetInput("X", std::vector<std::string>({conv_out->Name()}));
    desc.SetInput("Y", std::vector<std::string>({eltwise_y_in_node->Name()}));
    desc.SetOutput("Out", std::vector<std::string>({ac_out->Name()}));
    desc.SetType("elementwise_add");
    desc.SetAttr("axis", 1);
    auto eltwise_op = g->CreateOpNode(&desc);  // OpDesc will be copied.

    GraphSafeRemoveNodes(graph.get(), {ac_scale, ac_bias, affine_channel});

    IR_NODE_LINK_TO(conv_out, eltwise_op);
    IR_NODE_LINK_TO(eltwise_y_in_node, eltwise_op);
    IR_NODE_LINK_TO(eltwise_op, ac_out);
    found_conv_ac_count++;
  };

  gpd(graph.get(), handler);

  AddStatis(found_conv_ac_count);
  return graph;
}

std::unique_ptr<ir::Graph> ConvEltwiseAddAffineChannelFusePass::ApplyImpl(
    std::unique_ptr<ir::Graph> graph) const {
  PADDLE_ENFORCE(graph.get());
  FusePassBase::Init(name_scope_, graph.get());

  auto* scope = param_scope();
  PADDLE_ENFORCE(scope);

  GraphPatternDetector gpd;
  auto* conv_input =
      gpd.mutable_pattern()
          ->NewNode(patterns::PDNodeName(name_scope_, "conv_input"))
          ->AsInput()
          ->assert_is_op_input("conv2d", "Input");
  patterns::ConvAffineChannel conv_ac_pattern(gpd.mutable_pattern(),
                                              name_scope_);
  conv_ac_pattern(conv_input, true /*with_eltwise_add*/);

  int found_conv_ac_count = 0;
  auto handler = [&](const GraphPatternDetector::subgraph_t& subgraph,
                     Graph* g) {
    VLOG(4) << "handle ConvBN fuse";

    GET_CONV_BN_NODES(conv_ac_pattern);
    // OPERATORS
    GET_IR_NODE_FROM_SUBGRAPH(eltwise, eltwise, conv_ac_pattern);
    // BIAS inputs
    GET_IR_NODE_FROM_SUBGRAPH(eltwise_y_in, eltwise_y_in, conv_ac_pattern);
    // BIAS outputs
    GET_IR_NODE_FROM_SUBGRAPH(eltwise_out, eltwise_out, conv_ac_pattern);

    // Get eltwise_y (conv bias) variable
    auto* eltwise_y_in_tensor =
        scope->FindVar(eltwise_y_in->Name())->GetMutable<LoDTensor>();

    // Get batch norm bias
    auto* ac_bias_tensor =
        scope->FindVar(ac_bias->Name())->GetMutable<LoDTensor>();

    recompute_bias_and_weights(scope, conv_weight, *ac_scale, *ac_bias_tensor,
                               eltwise_y_in_tensor);

    // Update the elementwise_add node
    eltwise->Op()->SetAttr("axis", 1);
    eltwise->Op()->SetOutput("Out", std::vector<std::string>({ac_out->Name()}));

    GraphSafeRemoveNodes(graph.get(),
                         {ac_scale, ac_bias, affine_channel, eltwise_out});

    IR_NODE_LINK_TO(eltwise, ac_out);

    found_conv_ac_count++;
  };

  gpd(graph.get(), handler);
  AddStatis(found_conv_ac_count);
  return graph;
}

}  // namespace ir
}  // namespace framework
}  // namespace paddle

REGISTER_PASS(conv_affine_channel_fuse_pass,
              paddle::framework::ir::ConvAffineChannelFusePass);
REGISTER_PASS(conv_eltwiseadd_affine_channel_fuse_pass,
              paddle::framework::ir::ConvEltwiseAddAffineChannelFusePass);